Ordinary least squares

Known as: Ordinary Least Squares Regression, Least-squares estimation of linear regression coefficients, Linear least squares 
In statistics, ordinary least squares (OLS) or linear least squares is a method for estimating the unknown parameters in a linear regression model… (More)
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Topic mentions per year

Topic mentions per year

1962-2017
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Papers overview

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2017
2017
Linear regression is one of the most prevalent techniques in machine learning; however, it is also common to use linear… (More)
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2016
2016
We consider statistical aspects of solving large-scale least-squares (LS) problems using randomized sketching algorithms. For a… (More)
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2013
2013
  • Ata Kabán
  • 2013 IEEE 13th International Conference on Data…
  • 2013
The prospect of carrying out data mining on cheaply compressed versions of high dimensional massive data sets holds tremendous… (More)
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Highly Cited
2013
Highly Cited
2013
In this paper, we propose a generative tracking method based on a novel robust linear regression algorithm. In contrast to… (More)
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2013
2013
We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one simply projects the data onto… (More)
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Highly Cited
2010
Highly Cited
2010
Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several… (More)
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2010
2010
This paper gives an insight into the working and efficiency of the two basic algorithms used for parameter estimation: Ordinary… (More)
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2006
2006
This note formalizes bias and inconsistency results for ordinary least squares (OLS) on the linear probability model and provides… (More)
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Highly Cited
2002
Highly Cited
2002
The purpose of model selection algorithms such as All Subsets, Forward Selection, and Backward Elimination is to choose a linear… (More)
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1997
1997
  • Mark J. Jensen
  • 1997
We develop an ordinary least squares estimator of the long memory parameter from a fractionally integrated process that is an… (More)
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